OntoBayes: An Ontology-Driven Uncertainty Model

Yi, Yang; Calmet, Jacques

doi:10.1109/cimca.2005.1631307

Cited by 58 publications

(43 citation statements)

References 6 publications

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“…For instance, Lukasiewicz and Straccia [25] summarized approaches to manage uncertainty and vagueness in description logics for the semantic web. Ding et al [26] used Bayesian theories to manage context uncertainties; a similar approach was adopted by Yang et al [27]. An approach to assess the ambiguity of context data was proposed by [28] using probabilities and fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Martínez-Ortega

Rubio

2017

Applied Sciences

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Abstract:Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots.

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Section: Related Workmentioning

confidence: 99%

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Martínez-Ortega

Rubio

2017

Applied Sciences

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“…Yang and Calmet [282] present an integration of the web ontology language OWL with Bayesian networks, called OntoBayes. The approach makes use of probability and dependency-annotated OWL to represent uncertain information in Bayesian networks.…”

Section: Semantics Now We Define the Semantics Of P-shoin (D)mentioning

confidence: 99%

Managing Uncertainty and Vagueness in Description Logics, Logic Programs and Description Logic Programs

Straccia

2008

Lecture Notes in Computer Science

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“…Uncertainty in Semantic Web ontologies has been addressed in BayesOWL [6] and OntoBayes [31]. Furthermore, PR-OWL [4] is a Bayesian Ontology Language for the Semantic Web.…”

Section: Related Workmentioning

confidence: 99%

“…However, the LOD cloud includes many sources with varying reliability and to correctly account for data veracity remains a big challenge. To address this issue, reasoning with inconsistent and uncertain ontologies has recently emerged as a research field of its own [6,31,4,9,3,15]. In this paper we approach the veracity issue from the perspective of probabilistic databases (PDB), which consider multiple possible occurrences of a database via a possible worlds semantics and account for uncertainty in the data by assigning a probability distribution over all database instances [27].…”

Section: Introductionmentioning

confidence: 99%

Querying Factorized Probabilistic Triple Databases

Krompaβ

Nickel

Tresp

2014

Lecture Notes in Computer Science

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Abstract. An increasing amount of data is becoming available in the form of large triple stores, with the Semantic Web's linked open data cloud (LOD) as one of the most prominent examples. Data quality and completeness are key issues in many community-generated data stores, like LOD, which motivates probabilistic and statistical approaches to data representation, reasoning and querying. In this paper we address the issue from the perspective of probabilistic databases, which account for uncertainty in the data via a probability distribution over all database instances. We obtain a highly compressed representation using the recently developed RESCAL approach and demonstrate experimentally that efficient querying can be obtained by exploiting inherent features of RESCAL via sub-query approximations of deterministic views.

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OntoBayes: An Ontology-Driven Uncertainty Model

Cited by 58 publications

References 6 publications

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Managing Uncertainty and Vagueness in Description Logics, Logic Programs and Description Logic Programs

Querying Factorized Probabilistic Triple Databases

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